RCHOP

singlet <- NormalizeData(singlet, assay = "RNA")
singlet <- ScaleData(singlet, features = rownames(singlet))
response <- FindMarkers(singlet, ident.1 = item1, ident.2 = item2 , logfc.threshold = log2(FC), assay = "RNA", slot = "data", test.use = "bimod")

response.RCHOP <- response
save(response.RCHOP, file = "../Reponse/response.RCHOP.raw.Rdata")

response <- SC.Result(response)

DT.table(response)

# ES <- enrichIt(obj = singlet, gene.sets = GS, groups = 1000, cores = 8, min.size = 5)
# save(ES, file = "../Reponse/RCHOP_ES.RDs")

Excipient

singlet.exp <- NormalizeData(singlet.exp, assay = "RNA")
singlet.exp <- ScaleData(singlet.exp, features = rownames(singlet.exp))
response.exp <- FindMarkers(singlet.exp, ident.1 = item1, ident.2 = item2 , logfc.threshold = log2(FC), assay = "RNA", slot = "data", test.use = "bimod")


save(response.exp, file = "../Reponse/response.exp.raw.Rdata")

response.exp <- SC.Result(response.exp)

DT.table(response.exp)

# ES <- enrichIt(obj = singlet.exp, gene.sets = GS, groups = 1000, cores = 10, min.size = 5)
# save(ES, file = "../Reponse/Excipient_ES.RDs")

Pré-greffe

singlet.pg <- NormalizeData(singlet.pg, assay = "RNA")
singlet.pg <- ScaleData(singlet.pg, features = rownames(singlet.pg))
response.pg <- FindMarkers(singlet.pg, ident.1 = item1, ident.2 = item2 , logfc.threshold = log2(FC), assay = "RNA", slot = "data", test.use = "bimod")

save(response.pg, file = "../Reponse/response.pg.raw.Rdata")

response.pg <- SC.Result(response.pg)




DT.table(response.pg)

ES <- enrichIt(obj = singlet.pg, gene.sets = GS, groups = 1000, cores = 10, min.size = 5)

## [1] "Using sets of 1000 cells. Running 21 times."
## Setting parallel calculations through a SnowParam back-end
## with workers=10 and tasks=100.
## Estimating ssGSEA scores for 50 gene sets.
## [1] "Calculating ranks..."
## [1] "Calculating absolute values from ranks..."
## Setting parallel calculations through a SnowParam back-end
## with workers=10 and tasks=100.
## Estimating ssGSEA scores for 50 gene sets.
## [1] "Calculating ranks..."
## [1] "Calculating absolute values from ranks..."
## Setting parallel calculations through a SnowParam back-end
## with workers=10 and tasks=100.
## Estimating ssGSEA scores for 50 gene sets.
## [1] "Calculating ranks..."
## [1] "Calculating absolute values from ranks..."
## Setting parallel calculations through a SnowParam back-end
## with workers=10 and tasks=100.
## Estimating ssGSEA scores for 50 gene sets.
## [1] "Calculating ranks..."
## [1] "Calculating absolute values from ranks..."
## Setting parallel calculations through a SnowParam back-end
## with workers=10 and tasks=100.
## Estimating ssGSEA scores for 50 gene sets.
## [1] "Calculating ranks..."
## [1] "Calculating absolute values from ranks..."
## Setting parallel calculations through a SnowParam back-end
## with workers=10 and tasks=100.
## Estimating ssGSEA scores for 50 gene sets.
## [1] "Calculating ranks..."
## [1] "Calculating absolute values from ranks..."
## Setting parallel calculations through a SnowParam back-end
## with workers=10 and tasks=100.
## Estimating ssGSEA scores for 50 gene sets.
## [1] "Calculating ranks..."
## [1] "Calculating absolute values from ranks..."
## Setting parallel calculations through a SnowParam back-end
## with workers=10 and tasks=100.
## Estimating ssGSEA scores for 50 gene sets.
## [1] "Calculating ranks..."
## [1] "Calculating absolute values from ranks..."
## Setting parallel calculations through a SnowParam back-end
## with workers=10 and tasks=100.
## Estimating ssGSEA scores for 50 gene sets.
## [1] "Calculating ranks..."
## [1] "Calculating absolute values from ranks..."
## Setting parallel calculations through a SnowParam back-end
## with workers=10 and tasks=100.
## Estimating ssGSEA scores for 50 gene sets.
## [1] "Calculating ranks..."
## [1] "Calculating absolute values from ranks..."
## Setting parallel calculations through a SnowParam back-end
## with workers=10 and tasks=100.
## Estimating ssGSEA scores for 50 gene sets.
## [1] "Calculating ranks..."
## [1] "Calculating absolute values from ranks..."
## Setting parallel calculations through a SnowParam back-end
## with workers=10 and tasks=100.
## Estimating ssGSEA scores for 50 gene sets.
## [1] "Calculating ranks..."
## [1] "Calculating absolute values from ranks..."
## Setting parallel calculations through a SnowParam back-end
## with workers=10 and tasks=100.
## Estimating ssGSEA scores for 50 gene sets.
## [1] "Calculating ranks..."
## [1] "Calculating absolute values from ranks..."
## Setting parallel calculations through a SnowParam back-end
## with workers=10 and tasks=100.
## Estimating ssGSEA scores for 50 gene sets.
## [1] "Calculating ranks..."
## [1] "Calculating absolute values from ranks..."
## Setting parallel calculations through a SnowParam back-end
## with workers=10 and tasks=100.
## Estimating ssGSEA scores for 50 gene sets.
## [1] "Calculating ranks..."
## [1] "Calculating absolute values from ranks..."
## Setting parallel calculations through a SnowParam back-end
## with workers=10 and tasks=100.
## Estimating ssGSEA scores for 50 gene sets.
## [1] "Calculating ranks..."
## [1] "Calculating absolute values from ranks..."
## Setting parallel calculations through a SnowParam back-end
## with workers=10 and tasks=100.
## Estimating ssGSEA scores for 50 gene sets.
## [1] "Calculating ranks..."
## [1] "Calculating absolute values from ranks..."
## Setting parallel calculations through a SnowParam back-end
## with workers=10 and tasks=100.
## Estimating ssGSEA scores for 50 gene sets.
## [1] "Calculating ranks..."
## [1] "Calculating absolute values from ranks..."
## Setting parallel calculations through a SnowParam back-end
## with workers=10 and tasks=100.
## Estimating ssGSEA scores for 50 gene sets.
## [1] "Calculating ranks..."
## [1] "Calculating absolute values from ranks..."
## Setting parallel calculations through a SnowParam back-end
## with workers=10 and tasks=100.
## Estimating ssGSEA scores for 50 gene sets.
## [1] "Calculating ranks..."
## [1] "Calculating absolute values from ranks..."
## Setting parallel calculations through a SnowParam back-end
## with workers=10 and tasks=100.
## Estimating ssGSEA scores for 50 gene sets.
## [1] "Calculating ranks..."
## [1] "Calculating absolute values from ranks..."

save(ES, file = "../Reponse/Pre-greffe_ES.RDs")

RCHOP

load(file = "../Reponse/RCHOP_ES.RDs")

singlet <- AddMetaData(singlet, ES)
singlet@meta.data$active.idents <- singlet@active.ident

HM <- data.frame(singlet[[colnames(ES)]], Idents(singlet)) ; colnames(HM)[ncol(HM)] <- condition
HM.Result <- getSignificance(HM, group = condition, fit = "Wilcoxon") 
HM.Result <- HM.Result[order(HM.Result$FDR),]
HM.Result <- HM.Result[HM.Result$FDR<0.05,]

HM.SC.Result.raw.RCHOP <- HM.Result
save(HM.SC.Result.raw.RCHOP, file = "../Reponse/HM.SC.Result.raw.RCHOP.Rdata")


HM.RCHOP.Result <- SC.Enrichissement(singlet, ES, condition)
DT.table(HM.RCHOP.Result)

Excipient

load(file = "../Reponse/Excipient_ES.RDs")

singlet.exp <- AddMetaData(singlet.exp, ES)
singlet.exp@meta.data$active.idents <- singlet.exp@active.ident

HM <- data.frame(singlet.exp[[colnames(ES)]], Idents(singlet.exp)) ; colnames(HM)[ncol(HM)] <- condition
HM.Result <- getSignificance(HM, group = condition, fit = "Wilcoxon") 
HM.Result <- HM.Result[order(HM.Result$FDR),]
HM.Result <- HM.Result[HM.Result$FDR<0.05,]

HM.SC.Result.raw.Excipient <- HM.Result
save(HM.SC.Result.raw.Excipient, file = "../Reponse/HM.SC.Result.raw.Excipient.Rdata")


HM.Excipient.Result <- SC.Enrichissement(singlet.exp, ES, condition)
DT.table(HM.Excipient.Result)

Pré-greffe

load(file = "../Reponse/Pre-greffe_ES.RDs")

singlet.pg <- AddMetaData(singlet.pg, ES)
singlet.pg@meta.data$active.idents <- singlet.pg@active.ident

HM <- data.frame(singlet.pg[[colnames(ES)]], Idents(singlet.pg)) ; colnames(HM)[ncol(HM)] <- condition
HM.Result <- getSignificance(HM, group = condition, fit = "Wilcoxon") 
HM.Result <- HM.Result[order(HM.Result$FDR),]
HM.Result <- HM.Result[HM.Result$FDR<0.05,]

HM.SC.Result.raw.PG <- HM.Result
save(HM.SC.Result.raw.PG, file = "../Reponse/HM.SC.Result.raw.PG.Rdata")

HM.PG.Result <- SC.Enrichissement(singlet.pg, ES, condition)
DT.table(HM.PG.Result)

Diagram

Liste

Reduce(intersect, list(HM.PG.Result$Hallmark,HM.RCHOP.Result$Hallmark,HM.Excipient.Result$Hallmark))

##  [1] "HALLMARK_MITOTIC_SPINDLE"                  
##  [2] "HALLMARK_TNFA_SIGNALING_VIA_NFKB"          
##  [3] "HALLMARK_G2M_CHECKPOINT"                   
##  [4] "HALLMARK_HEME_METABOLISM"                  
##  [5] "HALLMARK_INTERFERON_GAMMA_RESPONSE"        
##  [6] "HALLMARK_MYOGENESIS"                       
##  [7] "HALLMARK_MTORC1_SIGNALING"                 
##  [8] "HALLMARK_UNFOLDED_PROTEIN_RESPONSE"        
##  [9] "HALLMARK_COAGULATION"                      
## [10] "HALLMARK_ESTROGEN_RESPONSE_EARLY"          
## [11] "HALLMARK_APICAL_JUNCTION"                  
## [12] "HALLMARK_IL2_STAT5_SIGNALING"              
## [13] "HALLMARK_KRAS_SIGNALING_DN"                
## [14] "HALLMARK_IL6_JAK_STAT3_SIGNALING"          
## [15] "HALLMARK_KRAS_SIGNALING_UP"                
## [16] "HALLMARK_HEDGEHOG_SIGNALING"               
## [17] "HALLMARK_HYPOXIA"                          
## [18] "HALLMARK_PROTEIN_SECRETION"                
## [19] "HALLMARK_APOPTOSIS"                        
## [20] "HALLMARK_COMPLEMENT"                       
## [21] "HALLMARK_ANDROGEN_RESPONSE"                
## [22] "HALLMARK_CHOLESTEROL_HOMEOSTASIS"          
## [23] "HALLMARK_ALLOGRAFT_REJECTION"              
## [24] "HALLMARK_PI3K_AKT_MTOR_SIGNALING"          
## [25] "HALLMARK_ANGIOGENESIS"                     
## [26] "HALLMARK_INFLAMMATORY_RESPONSE"            
## [27] "HALLMARK_PEROXISOME"                       
## [28] "HALLMARK_XENOBIOTIC_METABOLISM"            
## [29] "HALLMARK_E2F_TARGETS"                      
## [30] "HALLMARK_MYC_TARGETS_V2"                   
## [31] "HALLMARK_MYC_TARGETS_V1"                   
## [32] "HALLMARK_INTERFERON_ALPHA_RESPONSE"        
## [33] "HALLMARK_UV_RESPONSE_UP"                   
## [34] "HALLMARK_GLYCOLYSIS"                       
## [35] "HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY"  
## [36] "HALLMARK_DNA_REPAIR"                       
## [37] "HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION"
## [38] "HALLMARK_OXIDATIVE_PHOSPHORYLATION"        
## [39] "HALLMARK_FATTY_ACID_METABOLISM"            
## [40] "HALLMARK_BILE_ACID_METABOLISM"             
## [41] "HALLMARK_ADIPOGENESIS"